Multimodal prognostic models and recent therapeutic advances
Authors: Andrea Minervini, Andrea Mari
Journal: The Lancet Digital Health (2023)

In The Lancet Digital Health, Cheng-Peng Gui and colleagues1Gui C-P Chen Y-H Zhao H-W et al.Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study.Lancet Digit Health. 2023; (published online June 29.)https://doi.org/10.1016/S2589-7500(23)00095-XGoogle Scholar present a crucial advancement in the field of renal cell carcinoma prognosis. In the age of personalised medicine, the ability to categorise patients based on a precise risk assessment is integral to effective treatment approaches. Gui and colleagues excel in their intent to develop a multimodal recurrence scoring system that incorporates both conventional clinical factors and emerging molecular markers. Clear cell renal cell carcinoma presents as a disease with various courses and treatment responses. Existing prognostic models, such as the Stage, Size, Grade, and Necrosis (SSIGN) score,2Frank I Blute ML Cheville JC Lohse CM Weaver AL Zincke H An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score.J Urol. 2002; 168: 2395-2400Crossref PubMed Google Scholar UCLA Integrated Staging System,3Tan M-H Kanesvaran R Li H et al.Comparison of the UCLA integrated staging system and the Leibovich score in survival prediction for patients with nonmetastatic clear cell renal cell carcinoma.Urology. 2010; 75: 1365-1370Summary Full Text Full Text PDF PubMed Scopus (33) Google Scholar and International Metastatic Renal Cell Carcinoma Database Consortium model,4Heng DYC Xie W Regan MM et al.External validation and comparison with other models of the International Metastatic Renal-Cell Carcinoma Database Consortium prognostic model: a population-based study.Lancet Oncol. 2013; 14: 141-148Summary Full Text Full Text PDF PubMed Scopus (667) Google Scholar offer substantial prognostic data, but their constraints have been exposed. These traditional models predominantly rely on clinical and pathological variables, overlooking the abundant prognostic insights that the molecular profile of a tumour can offer.5Lastraioli E Pillozzi S Mari A et al.hERG1 and CA IX expression are associated with disease recurrence in surgically resected clear cell renal carcinoma.Eur J Surg Oncol. 2020; 46: 209-215Summary Full Text Full Text PDF Google Scholar In the setting of personalised medicine, patient segregation based on precise risk predictions remains pivotal to effectual treatment strategies. The phase 3 KEYNOTE-564 trial6Choueiri TK Tomczak P Park SH et al.Adjuvant pembrolizumab after nephrectomy in renal-cell carcinoma.N Engl J Med. 2021; 385: 683-694Crossref PubMed Google Scholar has supported the use of adjuvant pembrolizumab in patients with high-risk renal cell carcinoma, highlighting the need for precise patient stratification to avoid overtreatment or undertreatment. The research demonstrated the development of a multimodal recurrence scoring system that combines conventional clinical factors with new molecular markers to address this need. Although the study by Gui and colleagues1Gui C-P Chen Y-H Zhao H-W et al.Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study.Lancet Digit Health. 2023; (published online June 29.)https://doi.org/10.1016/S2589-7500(23)00095-XGoogle Scholar adds substantial value, it also leaves room for further inquiries. The functionality of this multimodal scoring system needs external validation across various patient demographics and multiple medical institutions. Moreover, given the primary focus of the study on recurrence prediction, the system's proficiency in predicting other vital endpoints, such as cancer-specific survival and overall survival, warrants scrutiny. The study also brings up fascinating aspects related to temporal variations in the scoring system. As the disease advances, the factors contributing to the score might evolve, mirroring the changing character of renal cell carcinoma. Comprehending the implications of such modifications might offer deeper insights into the disease's biology and influence modifications in patient care. In the context of current research or practice, the investigation encompasses the shift towards a more comprehensive and integrative approach to risk categorisation in renal cell carcinoma. Although the research adopts a multimodal methodology, it does not include immunotherapy-related markers. Incorporating these markers could enhance the prognostic value of the model, considering their rising significance in predicting immunotherapeutic response, a crucial element in contemporary renal cell carcinoma treatment. Such a model might surpass traditional paradigms and, if subsequent studies affirm its robustness, this model could pave the way for more personalised risk evaluations and, in turn, customised management strategies. In conclusion, this research makes a significant contribution by introducing a promising scoring system that effectively reflects the intricate nature of renal cell carcinoma and might be supportive to the evolving renal cell carcinoma treatment landscape. The integration of deep learning technology in this model offers a refined methodology for understanding and predicting the disease course. Nevertheless, more validation is required, especially regarding patients undergoing treatment with modern immunotherapies. In the field of precision medicine, the integration of clinical data, molecular profiling, and sophisticated computational methodologies is imperative for advancement. This investigation represents a crucial waypoint on the demanding but rewarding path towards a more accurate determination of renal cell carcinoma prognosis. We declare no competing interests. Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation studyOur multimodal recurrence score is a practical and reliable predictor that can add value to the current staging system for predicting localised renal cell carcinoma recurrence after surgery, and this combined approach more precisely informs treatment decisions about adjuvant therapy. Full-Text PDF Open Access